All examined FSL methods are susceptible to class imbalance, although some show more robustness (e.g., Matching Net, Relation Net, and DKT). Optimization-based methods and fine-tune baselines suffer more as they use conventional supervised loss functions in the inner-loop which are known to be particularly susceptible to imbalance [1]}, [2]}, [3]}. Moreover, the problem of class imbalance persists as the backbone complexity and support set size increase. Those results suggest that current solutions will offer sub-optimal performance in real-world few-shot problems.
